Scaling and evaluating sparse autoencoders

We had done very extensive ablations at small scale where we found TopK to be consistently better than all of the alternatives we iterated through, and by the time we launched the big run we had already worked out how to scale all of the relevant hyperparameters, so we were decently confident.

One reason we might want a progressive code is it would basically let you train one autoencoder and use it for any k you wanted to at test time (which is nice because we don't really know exactly how to set k for maximum interpretability yet). Unfortunately, this is somewhat worse than training for the specific k you want to use, so our recommendation for now is to train multiple autoencoders.

Also, even with a progressive code, the activations on the margin would not generally be negative (we actually apply a ReLU to make sure that the activations are definitely non-negative, but almost always the (k+1)th value is still substantially positive)

Thanks for your kind words!

My views on interpretability are complicated by the fact that I think it's quite probable there will be a paradigm shift between current AI and the thing that is actually AGI like 10 years from now or whatever. So I'll describe first a rough sketch of what I think within-paradigm interp looks like and then what it might imply for 10 year later AGI. (All these numbers will be very low confidence and basically made up)

I think the autoencoder research agenda is currently making significant progress on item #1. The main research bottlenecks here are (a) SAEs might not be able to efficiently capture every kind of information we care about (e.g circular features) and (b) residual stream autoencoders are not exactly the right thing for finding circuits. Probably this stuff will take a year or two to really hammer out. Hopefully our paper helps here by giving a recipe to push autoencoders really quickly so we bump into the limitations faster and with less second guessing about autoencoder quality.

Hopefully #4 can be done to some great part in parallel with #1; there's a whole bunch of engineering needed to e.g take autoencoders and scale them up to capture all the behavior of the model (which was also a big part of the contribution of this paper). I'm pretty optimistic that if we have a recipe for #1 that we trust, the engineering (and efficiency improvements) for scaling up is doable. Maybe this adds another year of serial time. The big research uncertainty here fmpov is how hard it is to actually identify the structures we're looking for, because we'll probably have a tremendously large sparse network where each node does some really boring tiny thing.

However, I mostly expect that GPT-4 (and probably 5) is probably just actually not doing anything super spicy/stabby. So I think most of the value of doing this interpretability will be to sort of pull back the veil, so to speak, of how these models are doing all the impressive stuff. Some theories of impact:

• Maybe we'll become less confused about the nature of intelligence in a way that makes us just have better takes about alignment (e.g there will be many mechanistic theories of what the heck GPT-4 is doing that will have been conclusively ruled out)
• Maybe once the paradigm shift happens, we will be better prepared to identify exactly what interpretability assumptions it broke (or even just notice whether some change is causing a mechanistic paradigm shift)

Unclear what timeline these later things happen on; probably depends a lot on when the paradigm shift(s) happen.